Apple Paper Shows How to Convert Transformers to Mamba SSMs Without Retraining

A new Apple research paper describes a method for distilling Transformer models into Mamba-style state space models, enabling cheaper long-context inference without full retraining.

A paper from Apple, highlighted by @dair_ai, introduces a technique for converting pre-trained Transformer models into Mamba-architecture state space models (SSMs) without requiring expensive retraining from scratch. The approach, described as attention-to-Mamba distillation, preserves most of the original model's capabilities while dramatically reducing the compute cost of long-context inference.

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